System and Method for Set Top Viewing Data

ABSTRACT

In a video delivery context, collection and analysis of viewing data can provide insight into viewer interaction with video and the Internet. The viewing data can be transmitted in a controlled manner to a data repository. The system can selectively target specific viewers/households to obtain viewing data, which can be combined with demographics, anonymized, and encrypted. Embodiments enable precision selection of media opportunities, by determining detailed characteristics associated with broadcasts including movement of audiences and specific viewer behavior, such as visits to websites on the Internet. The effective yield of broadcasts, including promotional spots and advertisements, can be determined and predicted based on concrete data at a level of detail down to individual viewers. Accordingly, embodiments enable improvement of the effectiveness and return on investment for programming, promotional spots, and advertisements.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Appl. No.61/346,731 filed on May 20, 2010, entitled “SYSTEM AND METHOD FOR SETTOP BOX VIEWING DATA,” which is hereby incorporated by reference in itsentirety.

BACKGROUND

1. Field of the Invention

Embodiments relate generally to television or other video programmingviewing data received from a set top box, and more particularly tohandling of such viewing data including collection, transmission,storage, analysis, modification, and application of the viewing data.

2. Background Art

A set top box, or STB, is generally deployed at a viewer's household orbusiness (customer premise) to provide the viewer with the ability tocontrol delivery of video programming from a provider. The viewer canissue control commands to the STB, for example, powering on the STB,tuning to a specific channel, and tuning to other specific channels overtime. A customer can also send and receive data to and from theprovider, for example, via an Internet connection. A viewer maysimultaneously, but separately, interact with the Internet and videoprogramming. For example, the viewer can access the Internet in responseto video programming, either separately through a PC, or perhaps througha browser-enabled STB.

Conventional viewing data collection systems are limited in a number ofways: either in the number of viewers, in the number of viewer controlevents, or even excluding simultaneous or related Internet activity.Viewing data collection is typically limited to a particular set oftarget households, sampled at limited intervals. What is needed is anarrangement that enables significant flexibility and accuracy associatedwith viewing data, including tracking viewing data associated withbroadcast network video programming, as well as simultaneous or relatedInternet usage. Furthermore, what is needed is an arrangement thatenables a high degree of control and specificity associated withhandling of viewing data.

BRIEF DESCRIPTION OF THE DRAWINGS/FIGURES

The accompanying drawings are included to provide further understanding,are incorporated in and constitute a part of this specification, andillustrate embodiments that, together with the description, serve toexplain the principles of the invention. In the drawings:

FIG. 1 is a block diagram of an architecture of a video and Internetsystem including collection of viewing data according to an embodiment.

FIG. 2 is a block diagram of an architecture of a video system includingrandomized upload of viewing data according to an embodiment.

FIG. 3 is a block diagram of an architecture of a video system includingcollection of viewing data according to another embodiment.

FIG. 4 is an example chart based on viewing data according to anembodiment.

FIG. 5 is an example chart based on viewing data according to anembodiment.

FIG. 6 is a block diagram of an exemplary computer system on whichembodiments can be implemented.

The present embodiments will now be described with reference to theaccompanying drawings. In the drawings, like reference numbers mayindicate identical or functionally similar elements.

DETAILED DESCRIPTION

While the present invention is described herein with reference toillustrative embodiments for particular applications, it should beunderstood that the invention is not limited thereto. Those skilled inthe art with access to the teachings provided herein will recognizeadditional modifications, applications, and embodiments within the scopeof the invention and additional fields in which the invention would beof significant utility.

Collection and analysis of viewing data can provide a flexible,configurable, scalable method to capture STB and Internet events, andtransmit the viewing data in a controlled manner to a databaserepository, e.g., a data warehouse, head-end based repository, or thelike. The system can target specific viewers/households to obtainviewing data including Internet and STB usage, and can anonymize andencrypt the viewing data. The system has flexibility in controlling howand when collected viewing data is transferred to a repository,preventing spikes in network traffic.

Arrangements can collect anonymous viewing data from a sample ofhouseholds. The sample configuration can be chosen randomly or accordingto preselected criteria. The collected viewing data can be combined withsales, demographic and sociographic characteristics of the households,while maintaining the anonymity of the viewing households. Accordingly,the arrangement provides a very detailed understanding of viewingpatterns, including television programming, commercials, and Internetusage, while maintaining anonymity. The data is available for use on anear real-time basis.

The specific illustrations and embodiments are described with respect toa STB and/or television in connection with data within aservice-provider managed network. Embodiments also can be applied tovarious platforms including personal computers, cellular telephones,personal digital assistants (PDAs), tablets (e.g., Apple® iPad™), andother mobile devices, Internet Protocol (IP)-based telephones usingvoice over IP (VOID), digital video recorders (DVRs), remote-storageDVRs, video on-demand (VOD) systems, interactive TV systems, and othersystems capable of receiving and displaying video and/or utilizing anetwork connection such as the Internet. References to a STB shouldtherefore be interpreted to include these and other similar systemsinvolving display of video and viewer input.

Collection and Storage of Viewing Data

FIG. 1 illustrates a block diagram of an architecture of a video system100 including collection of viewing data from within a service-providermanaged network according to an embodiment. Video system 100 includesSTB 102. Exemplary STB 102 can include, without limitation, an InternetProtocol (IP)-based (i.e., IPTV) STB. Embodiments are not limited tothis exemplary STB, and it would be apparent to those skilled in the artthat other STBs can be used in embodiments described herein, includingpersonal computers. Many additional STBs can be used with the system100, although only STB 102 is illustrated in FIG. 1. A network-connecteddevice separate from STB 102, such as a personal computer, can beconnected to the Internet 105, although not specifically illustrated.Accordingly, the headend 101 can receive and monitor viewing dataassociated with viewer interaction with the Internet 105 or videoprogramming. STB 102 can also interact with viewing data.

STB 102 is configured to interact with media including receiving videobroadcast and the Internet 105 from headend 101. The STB 102 receivesoperational commands from a viewer, including tuning actions. A remotecontrol (not shown) may be used to control operation of STB 102. Someset-top boxes may have controls 103 thereon not requiring the use of aremote control. The remote control is configured with buttons to controlthe STB 106, including play, pause, stop, etc.

The STB 102 can be configured to receive usage collection configurationinformation (UC Config 104). As illustrated, UC Config 104 is sent fromthe headend 101, although UC Config 104 can be sent from alternatesources, e.g., via the Internet, and UC Config 104 can be stored on andupdated at the STB 102. The STB 102 can also be programmed to implementtools used for capturing viewing activity. STB 102 can receive updatesto re-program the STB 102 and associated tools.

UC Config 104 can instruct the STB 102 to selectively collect, store,and send viewing data 106. Programming information associated with videobroadcasts received from headend 101 can be included in viewing data106. Programming information can include, for example, a name of abroadcast program or promotional spot/advertisement, along with otheridentifying information. Viewing data 106 can also include operationalcommands from the viewer. Viewing data 106 can be associated with ahousehold, and can include an anonymous tracking number corresponding tothe household, specific equipment used, the date and time in seconds,the channel tuned, and the duration in seconds of the viewing of thatchannel before the channel was changed or the STB 102 was powered off.

Viewing data 106 can be collected without specific programminginformation such as the name of programming and advertisements/promos.Excluding such data can reduce the total amount of viewing data stored,transmitted, etc. The specific programming information can be recoveredby combining viewing data 106 with, for example, program content logsand as-run advertising logs. The specific programming information isaccurately correlated with the viewing data 106 based on, for example,timestamps included in the viewing data 106 and the logs.

Viewing data 106 can include additional data. For example, viewing data106 can include interaction with Internet 105. Headend 101 controlsaccess to Internet 105, and can therefore monitor viewer interactionover Internet 105 for storage with viewing data 106. Alternatively, STB102 can store viewing data 106 associated with viewer interaction withthe Internet 105. It is also contemplated that specific Internetconnected devices, separate from STB 102, can report viewing data 106 tothe headend 101, STB 102, data collectors 108, etc. along with timestampinformation that can be used to correlate the Internet activity.

In one embodiment, information about household members can be collectedand stored, for example at the head end or other repository. Informationcan include, without limitation, name, address (including zip code), andage. Other demographic and/or geographic information can also becollected. Alternatively, a third party can provide data. Also,information such as brand/type/model of television, phone, computer,PDA, mobile device, etc. could also be collected. This information canthen be associated with viewing data. However, the data can be madeanonymous (e.g., removing specifically identifying information).

STB 102 can be instructed to collect viewing data 106 continuously overtime. STB 102 can also be instructed to collect viewing data atintervals, or at specific times associated with a change in anyparameter including changes in viewing data, programming, promotionalspots, advertisements, and overlays.

For example, video programming from headend 101 can include interactiveoverlays. Overlays can include promotional information such as a link toa website. Because the video system 100 includes monitoring of Internet105, it is possible to collect data including a viewer's interactionwith Internet 105. Internet interaction data can be stored along withviewing data 106, and can also be maintained separately along withtimestamps for correlating the data over time.

STB 102 can be instructed to collect viewing data 106 associated withevents. For example, the STB 102 can collect a sampling of viewing data106 continuously or with the passage of time, e.g., every second. Theterm continuous is used herein to mean substantially uninterrupted. Theterm periodically means more often than on the half-an-hour. This allowsa more granular view of the viewing habit of a sample audience.Additionally, the STB 102 can collect viewing data 106 when the STB 102receives an operational command from the viewer, or when Internetactivity is detected. The STB 102 can also collect viewing data 106associated with a change in video broadcast programming. For example,the STB 102 can collect first viewing data 106 upon receiving anoperational command to tune to a specific broadcast channel, secondviewing data 106 associated with a first commercial break on the tunedchannel, third viewing data 106 associated with tuning to a differentchannel, fourth viewing data 106 associated with tuning back to theoriginal channel, and fifth viewing data 106 associated with Internetactivity. Events can be associated with a high priority, to ensure thatcollecting viewing data 106 is triggered by the occurrence of a highpriority event. Each data collection includes a timestamp, which can beused to correlate the data collection and also identify an amount oftime spent on an activity relative to the prior or next data collection(including turning off the STB 102).

The STB 102 can be configured to include a dwelling time associated witha duration of an activity, e.g., a duration that the STB 102 has beentuned to a channel. The dwelling time can be used as a factor indetermining a granularity associated with whether to collect viewingdata. For example, the STB 102 can be configured to include a dwellingtime of 10 seconds, such that viewing data 106 is recorded only forevents equal to or greater than 10 seconds in duration. Accordingly, theSTB 102 can exclude “channel surfing” sessions wherein a viewer rapidlytunes through a series of channels, spending less than the dwelling time(e.g., 10 seconds) viewing each channel. Accordingly, the STB 102 canconserve resources by storing viewing data that is more likely toreflect attentive viewing and not merely channel surfing. However, theSTB 102 can record all events continuously, and can utilize datacompression to reduce system storage and transmission loads, regardlessof whether “channel surfing” data is included.

The STB 102 can store the viewing data 106 in a format referred to asusage collection data events (UC Data), although many additional formatscan be used with the system 100. The system 100 can utilize compressionand record splitting, for example, to improve transmission and storageefficiency.

Viewing data 106 can also include information regarding the type ofequipment used by the viewer. For example, viewing data 106 can identifythe specific STB equipment, such as a brand and model of STB 102,including whether STB 102 is digital or IP-based. Although notspecifically illustrated, the system 100 can include a personal computeror the like, configured to interact with the Internet 105, receive videobroadcast from headend 101, or interact with a viewer for video controland display. Such devices can include cellular telephones, tabletcomputers, or other devices configured to interact with a network or theInternet 105. Accordingly, the personal computer can collect, store,send, and otherwise interact with viewing data 106 and Internet 105 aswould the STB 102. Headend 101 controls the data connection for Internet105, and therefore can monitor the Internet activity for a household orindividual viewer.

Such viewing and Internet activity can include activity initiated on aviewer's cellular telephone. For example, a cellular telephone caninteract with the Internet using a wireless Internet Protocol (IP)-basedconnection, e.g., IEEE 802.11 wireless local area network (WLAN)computer communication (“WIFI”). Also, a household can include awireless cellular network extender that utilizes the Internet 105 andallows cellular telephones to connect via their native wireless cellularconnection. Embodiments can also include Internet Protocol (IP)-basedtelephones using voice over IP (VoIP). Accordingly, video system 100 canmonitor many types of Internet activity at the household.

The system 100 can include very specific, individualized monitoring anddata collection. For example, viewers can be associated with a uniqueidentification (ID) or login. Examples of unique IDs include biometricssuch as fingerprint, retina scan, or the like, text-basedlogins/passwords, radio-frequency identification (RFID), cellulartelephone-based identification, or the like. The STB 102 can includereaders for monitoring and accepting such individual, unique IDs fromeach viewer. System 100 can also implement customizable icons or avatarsassociated with each unique ID, as well as the entire family orhousehold, to encourage viewers to login and utilize the unique IDs.

It is also contemplated that data associated with viewer equipment willbe associated with the viewer data 106. For example, the fact thatmultiple viewers access the Internet 105 via an Apple® computer afterviewing a commercial might suggest that an advertisement for Apple®would be appropriate for this time slot.

Viewing data 106 can be maintained anonymously. For example, viewingdata 106 can be collected without reference to metrics that can be usedto specifically identify a viewer or a household, such as an InternetProtocol (IP) number assigned to the household and associated with anaccount maintained by a resident at the household. Alternatively, allaspects of viewing data 106 can be collected, and specificallyidentifying metrics can be removed for anonymity.

Viewing data 106 can be stored at the STB 102 prior to transmission todata warehouse 110. Accordingly, network traffic can be managed byvarying the transmission of viewing data 106. UC Config 104 can instructthe STB 102 to selectively transmit the viewing data 106 to datacollectors 108. STB 102 can selectively transmit the viewing data 106based on various factors, such as the factors used for selectivecollection of viewing data 106 as set forth above. For example, viewingdata 106 can be transmitted once per day, at random times. It can alsobe sent periodically during the day (e.g., every half-an-hour) and/orsent at more- or less-frequent intervals. Viewing data 106 can betransmitted when a threshold amount of viewing data 106 has beencollected, when a storage buffer of the STB 102 reaches a specifiedcapacity, and/or when a transmission triggering event occurs. Thethreshold, specified capacity, and/or event can be adjustedindependently of specific hardware configurations. Data collectors 108can distribute the viewing data 106 to data warehouse 110. Asillustrated, data warehouse 110 can be in communication with headend101, such that headend 101 can interact with and monitor viewing data106 stored at data warehouse 110. Headend 101 can serve as a temporarycollection point, that then moves data to a data warehouse where it iscombined with other source data to create a rich profile of customerinteractions.

Reporting and analysis can be performed on the enriched, combined data.As illustrated, reporting module 112 can be in communication with datawarehouse 110 to provide various reports based on viewing data 106 atdata warehouse 110. Although one reporting module 112 is shown, multiplereporting modules can be provided in communication with variouscomponents of video system 100.

The data transmitted from STB 102 to the headend 101 may be encryptedprior to transmission for security purposes. The data transmitted fromSTB to the headend could then be made anonymous prior to encryption andtransmission.

Viewing data 106 can be identified by attribute and category. Suchidentifications can relate to measurable aspects of viewing data 106,and can also relate to aspects of viewing data 106 that are determinedby analysis (performed remotely and/or by the STB).

FIG. 2 illustrates a block diagram of an architecture of a video system200 including randomized upload of viewing data from within aservice-provider managed network according to an embodiment. System 200includes headend 201 configured to broadcast to STBs 202 and 205associated with households. For example, system 200 can involve a sampleof more than 50,000 STBs 202 and 205 from which viewing data can becollected.

A benefit of embodiments described herein is that arrangements caninvolve changeable sample sizes and selection. Accordingly, a sample canbe changed for various reasons. For example, if a particular demographic(e.g., age and household income) is not reflected in the sample, thesample can be changed to target that specific demographic. Additionally,the sample can be increased, to bring in more variety of householdcompositions and presumably include the target demographic in theincreased sample. Because headend 201 broadcasts to a large number ofhouseholds, any of those households, including all the households, canbe included. Selective targeting of STBs 202 and 205 provides improvedaccuracy and precision, valuable for promotional and advertisingpurposes.

Headend 201 can broadcast usage collection rules 204 to the STBs 202 and205. Usage collection rules 204 are evaluated by the STBs 202 and 205,and include rules regarding whether an STB should participate in datacollection and/or upload activity. FIG. 2 illustrates headend 201broadcasting the same usage collection rules 204 to all STBs 202 and 205associated with the headend 201. Alternatively, embodiments can involveselective broadcasting of the usage collection rules 204 to specificSTBs 202. Each of the STBs 202 and 205 evaluates the received usagecollection rules 204, and determines whether to opt-in for datacollection or upload activity based on applicable rules in the usagecollection rules 204. As illustrated, STBs 202 opt-in to data collectionactivity, and STBs 205 do not opt-in.

Evaluation of usage collection rules 204 can involve various factorsassociated with households, including STBs 202 and 205. Factors caninclude zip code (3-digit or 5-digit), percentage of an entire sample(e.g., a random selection of one percent of all households), type of STBor Internet device (e.g., model number, computer type), a node IDassociated with the STB, and other variables (e.g., variables stored ina computer or STB registry including serial number, MAC, home ID, etc.).Evaluation of the usage collection rules 204 can also involve Booleancombinations of factors. For example, usage collection rules 204 canspecify that selection=10% of households AND (zip code=117* OR 118*),where * is a wildcard representing any additional characters.

STBs 202 that opt-in to data collection activity can buffer the viewingdata and randomly upload the viewing data to data collection listener208. It is contemplated that STBs 202 that opt-in, and STBs 205 that donot opt-in, can buffer viewing data, and opting-in involves uploadingthe buffered viewing data. “Randomly” uploading can involve rules orintelligent algorithms that take into account aggregate network traffic,and predict the effect of uploading viewing data on the network.“Randomized” uploads can thereby distribute upload traffic among theSTBs 202 to avoid network use spikes in the aggregate, and uploads mayseem to occur randomly when considered from the perspective of anindividual STB 202.

FIG. 3 illustrates a block diagram of an architecture of a video system300 including collection of viewing data 306 from within aservice-provider managed network according to another embodiment. System300 includes data collection request system 301 that distributes a usagecollection file 304 to multiple STBs 302.

Based on the usage collection file 304, STBs 302 selectively collectand/or store usage collection data events and Internet usage events(viewing data 306). The viewing data 306 is collected by data collectors308, and exported to data warehouse 310. Reporting module 312 can be incommunication with data warehouse 310 to provide various reports basedon viewing data 306 at data warehouse 310. Although one reporting module312 is shown, multiple reporting modules can be provided incommunication with various components and data of video system 300.

In accordance with the embodiments described above, an example systemcan collect second-by-second viewing records from many randomly selectedand anonymous households. This includes anonymous tracking of viewingdata including channel tuning events, capturing the date, time, channeland duration of the tuning event. This viewing data enables thestatistical calculation of individual program share and viewership atfrequent intervals (e.g., including an industry standard quarter-hourshare) to determine commercial program value, as well as time-seriestracking of anonymous sample households for the purpose of analyzingviewing behavior patterns.

Analysis and Use of Viewing Data

The anonymous viewing data from a selection of households can becombined with demographic and sociographic characteristics of thehouseholds on a de-identified basis. Anonymous information can includedemographic composition, income, education, composition of household,lifestyle, behavioral, and product purchasing characteristics ofhouseholds in a broadcast coverage area, without actually identifyingspecific individuals. Information can be organic, specifically anonymousviewer data relevant to providing cable, Internet and telecom services.Other information can be acquired via commercial sources, includingstandard household demographic data, purchased household segmentationdata. Enhanced lifestyle data can enable ranking ofnetworking/programming according to how likely audiences share thelifestyle characteristics of target consumers. The viewing data isthereby maintained anonymously, while providing the capability tounderstand and analyze the anonymous viewing data at a very detailedlevel, on a near real-time basis.

Data can be identified by metric name and category, identifications thatcan relate to aspects of information determined by analysis of viewingdata (performed remotely and/or by the STB) and demographic andsociographic information/characteristics. The metrics, and otherdata/attributes, can be organized, analyzed, and presented in reports.The analysis can be done automatically, for example using a programmablemachine learning algorithm to predict future behavior. Reports can bemerged with various sort systems to manipulate the data for streamlinedpresentation of unique data and business intelligence, includingmicro-behavior analysis. Reports can be provided by reporting modules112 and 312 as shown in FIGS. 1 and 3, respectively. Multiple reportingmodules can be provided, using the metrics and other data/attributesavailable throughout the exemplary video systems.

Reports can be generated based on categories such as network, householdcount,

STB count, hours viewed count, total household percentage, total STBpercentage, and hours viewed percentage. The data provides additionaloptions for creating reports, including selecting start/end dates, acollection task sample (e.g., a sample of one percent of total), videotier, region (e.g., specific metro areas, broadcast coverage areas,product/sales region hierarchies), network, parent network, demographicattribute, zip code, start/end channel durations, cumulative duration(e.g., apply a 5/15 minute filter), and number of programs to include.Reports can be ranked based on various criteria/attributes to showdetails on the viewing data.

A first example report, audience by network (ranked by household count),can provide a list of networks in rank order of number of householdsviewing at any point during a user-selected time period. The firstexample report can be used to assess a specific network's reach during aperiod in time, and can be used to compare and contrast a set ofnetworks. Ranking by household count can result in, for example, a newschannel obtaining a high ranking. This ranking can be used to determinethat households typically tune in to watch news. Additional details onviewer behavior regarding the news channel can be determined by usingadditional reports/ranking.

A second example report, audience by network (ranked by viewingduration), can provide a list of networks in rank order of number ofhours viewed during a user-selected time period. The second examplereport can be used to assess a specific network's capture of viewingtime during a period, and can be used to compare and contrast a set ofnetworks. Ranking by viewing duration can result in, for example, thenews channel obtaining a middle ranking. This ranking can be used todetermine that households typically tune in to watch news for a briefduration, and then tune away. Various reports and rankings can becombined to develop very detailed micro-behaviors of audiences regardingviewed content.

A third example report, audience by network—combined SD-HD (ranked byhousehold count), can be based on the first example report and also canautomatically combine the high-definition (HD) and standard-definition(SD) version statistics for networks with HD versions. The third examplereport can be used, regardless of the split of viewership between HD andSD formats, to assess and compare/contrast networks as in the firstexample report. In the third example report, HD and SD viewership foreach network can be represented by a single combined entry.

A fourth example report, audience by network—combined SD-HD (ranked byviewing duration), can be based on the second example report and alsocan automatically combine the HD and SD version statistics for networkswith HD versions. The fourth example report can be used, regardless ofthe split of viewership between HD and SD formats, to assess andcompare/contrast networks as in the second example report. In the fourthexample report, HD and SD viewership for each network can be representedby a single combined entry.

A fifth example report, quarter hour share and rating, can be used tocalculate ratings and/or a share of viewers for user-selected networksduring each quarter-hour of a user-selected time period. The fifthexample report can be used to assess a specific network's share/ratingsover time, and to compare and contrast a set of networks. The fifthexample report can be output as a graph depicting the household count,percent of household count (share), and rating for selected networksduring each quarter hour period between selected start and end dates.The fifth example report can include combined SD/HD version statisticsfor networks with HD versions.

Additional example reports can be generated based on variouscriteria/categories of viewing data, including demographics. Anadditional report, audience by network with demographics, can describeviewers by network during a selected time period and according toselected demographic characteristics. This report can be used to assessa specific network's audience composition, comparing and contrasting aset of networks. Another report, program demographics, can describe theviewing audience for programs during a selected time period according toselected demographic characteristics. This report can be used to assessa specific program's audience composition, and can be used to compareand contrast a set of programs. Another report, quarter hour share withdemographics, can be used as an enhancement to the fifth example report(quarter hour share and rating). This report can be used to calculateshare of viewers for selected networks during each quarter-hour of aselected time period, adding the ability to select a specificdemographic attribute to narrow the audience measured. It can be used toassess a specific network's share over time within a specificdemographic, and to compare and contrast a set of networks. Reports canprovide insights into viewing data by ranking each attribute anddetermining how it shows in an audience.

Viewing data can provide powerful diagnostics to television programmers.The significant sample size allows for understanding of smalldifferences in actual viewing behavior within the viewing universes ofnumerous broadcast channels. Advertisers can target viewercharacteristics using detailed selection to optimize ad inventory.Programmers can identify specific insights and applications, relative totheir own audiences and their competitors' audiences.

Accordingly, it is possible to optimize proof of performance andbusiness intelligence. Regarding proof of performance, embodimentsenable collecting of specific and detailed data to show that advertisersare justified in their spending for ads that reach their intendedtargets. Regarding business intelligence, embodiments enable specificand detailed data associated with expected yields for a given day/timead or promo slot. Accordingly, a broadcaster can more accurately price agiven timeslot based on specific data. Furthermore, a broadcaster canadvise prospective advertisers as to which timeslots to target, based onthe demographics, content to be broadcast, and other factors.Accordingly, ads and promos can be matched with targeted timeslots at anextremely accurate and precise level, based on detailed viewing dataand/or sample selection of demographics.

FIG. 4 illustrates an example chart 400 based on viewing data accordingto an embodiment. Chart 400 illustrates the number of tuneaways 402 overtime during a commercial break. The tuneaways 402 are associated withPromotional (Promo) spots A-C and Ads 1-8 occurring over time asidentified by timestamps 404. Tuneaways 402 are incidents where a viewerissues a command to change the currently viewed channel. Embodiments ofthe system can include promo and ad descriptive information in theviewing data, along with timestamps. Embodiments can also obtain thepromo and ad descriptive information from program content logs andas-run advertising logs, which identify the Promo spots A-C and Ads 1-8with respect to timestamps 404 corresponding to timestamps of theviewing data.

Tuneaways 402 obtained from in-depth viewing data can therefore providedetailed viewer behavior throughout a broadcast, including duringcommercials. Tuneaways 402 at the second-by-second level allow mappingaudience behavior to each second of programming and advertising content.Similar behavior can be obtained for Internet or telephonic activityassociated with the household. For example, viewer data will indicatethat a viewer watching a sports program tuned to a comedy program afterten minutes. This type of data can provide valuable insight if a largeenough percentage of the sample audience did the same thing.

Analysis of viewing data enables the determination of detailed viewerbehavior over time, including the ability to link events within ahousehold. Household attributes include number of episodes viewed andnumber of minutes viewed. It is possible to create viewer profiles byusing household attributes. Embodiments can therefore segment a programaudience over an entire time span, e.g., broadcast season. Programaudience segments can include, for example, “addicted” viewers watchingeight or more premiere episodes live for a total of 300 or more minutes;“avid” viewers watching four to seven episodes for a total of 300 ormore minutes; “frequent flyers” watching five to eleven partialepisodes; “glued visitors” watching two to four mostly completeepisodes; and “light samplers” watching up to sixty minutes over two tofive episodes. Program audience segments enable identification ofaudience targeting opportunities. Therefore, it may be possible toreach, for example, two-thirds of active weekly premiere viewers bytargeting “frequent fliers” and “glued visitors”. Such information isvaluable for proof of performance and business intelligence purposes, innegotiations between broadcasters and advertisers, program development,and promotion.

Viewing data can be used to enable Set Top Box Media Planning andAllocation, which can allow programmers to plan media by highest ratedshows, networks, products and/or specific target audiencecharacteristics. Such metrics can be used to identify viewership in lowand non-rated networks. The metrics can also be used to maximize GrossRating Point (GRP) selection, e.g., the size of an audience reached by aspecific media vehicle or schedule, by finding and selecting across allnetworks, not just those that are highly rated.

Viewing data can also be used for Set Top Box Analytics, which can allowprogrammers and advertisers to find target audiences within the totalviewership (for example, on over 500 individual networks) based onspecific characteristics, identify competing networks, and find trendsin network viewership.

Embodiments can enable the planning and placement of a broadcaster'sinternal marketing (cable cross channel) spots and external advertisercommercial spots. Planning and placement can be based on identificationof characteristics of the target audience, using audience sharecalculations for each network of insertion by advertising zone or otherad insertion configuration, including specific dates and times.

Embodiments can also enable ranking of programs and networks based onshare and viewership calculations. Ranking can allow maximum utilizationof insertion inventory across the networks into which advertisements areinserted for a broadcaster or on behalf of external advertisers, byadvertising zone or other ad insertion configuration.

Embodiments can report the actual movement of anonymous audiences inresponse to what they are watching, either programs or advertisements,including Internet activity. Multiple viewing events from a singlehousehold can be linked together in time series, and correlated withInternet or other activities.

The various analyses of the embodiments described herein providediagnostic data to expand audience and behavioral insights. For example,embodiments can determine details regarding broadcasted programs,series, and channels. The details can include anonymously determiningwho is watching what at the program level, within and across series andchannels. Embodiments enable determining statistical differences betweenaudiences for individual programs; and the regularity with whichindividual viewing households view series installments.

Embodiments can determine details regarding audience composition andbehavior. For example, embodiments can determine audience flow,including determining from where audiences tuned when arriving at achannel, and to where audiences tune when departing the channel. Suchinformation enables coordinating the information from as-run ad logswith tuneaways at a second-by-second level, for example. Embodiments caninclude audience discovery, determining what would be a typical viewerof a given program or advertisement, and determining what other programsor advertisements the viewer typically watches. For example, weeklypremiere viewers of a show can be identified and profiled for reachingpotential viewers. Embodiments can determine audience behavior,including the loyalty of a given household/viewer to a specific channelas compared to its competitors. Determining audience behavior can alsoinclude determining percentage of total viewing time spent on a specificchannel versus its competitors. Audience behavior can be used to assessaudience loyalty via viewing frequency analysis, to create loyaltysegments.

Embodiments can also enable audience hunting, including examiningviewing habits of active viewer segments to identify promo placementopportunities to reach potential viewers. Therefore, it is possible tounderstand and maximize the current audience, and find and attractundiscovered audiences. Regarding the current anonymous audience, it ispossible to determine what else the current anonymous audience iswatching. It is also possible to determine where and how to entice thecurrent anonymous audience to watch more often. Regarding undiscoveredaudiences, it is possible to determine other viewers having interestssimilar to the current anonymous audience, including what the otherviewers are watching and where/how to entice them to discover and watcha given program or advertising.

Detailed audience intelligence, determined by analyzing viewing data,can be used not only for content development and creation, but also inassisting programmers to find the appropriate audience foralready-developed content. By using the viewing data to determinedetailed audience composition, behavior, preferences, etc., contentdevelopers can customize the content and programming that they invest indeveloping. For example, a content developer can determine that anaudience is receptive to a particular product placement, actor, or othercontent/feature based on viewing data. The content developer canincorporate that product placement and/or actor in the content beingdeveloped, and the audience is likely to be more receptive to thatcontent. The richness of viewing data provides analysis on a detailed,micro-behavioral level for an audience. Detailed intelligence onaudiences can enable the content developer to increase the likelihoodthat many detailed aspects of programming content will be successfullyreceived, and decrease the likelihood of developing a show, content,programming, etc. that “flops” when broadcast to an audience.

Embodiments can also determine promo effectiveness to improve return oninvestment and anonymous micro-behavior insights. For example,embodiments can determine sub-program level analyses, minute by minuteor second by second. Accordingly, it is possible for embodiments todetermine how well a given program or advertisement holds an anonymousaudience, or stimulates tune-ins to that program or advertisement. It isalso possible to determine the effect of devices such as interactiveoverlays, based on the corresponding Internet activity that correlateswith the timing of the interactive overlay. By determining which promospots were associated with a higher tune-in rate, promo effectivenessand return on investment can be improved.

Embodiments can facilitate the use of data collected and/or analyzed innear real-time, for specifically targeting advertising and promotions tothe particular audience while using the STB. For example, viewing datafrom a STB and Internet usage can be collected, and based on the data,ads and promos can be served to the STB in near real-time. Accordingly,embodiments can maximize the effectiveness of promos and ads, without aneed to predict in advance the target audience likely to be using theSTB.

FIG. 5 is an example chart 500 based on viewing data according to anembodiment. Viewing data can be analyzed for viewership of specificpromo spots 502, with respect to other viewing patterns of those usersviewing promo spots 502. For example, viewership of a specific promospot 502 can be compared with viewership of the promoted program, andother of the specific promo spots 502, to determine which of theanonymous viewers of the promo spots 502 eventually watched the promoteditem. For example, analyzing viewing data can enable identification ofviewers of promo spots over various days of the week leading up to theairing of the promoted program. When the program is broadcast, viewingdata can enable identification of viewers of the program who previouslyviewed the promo spots.

Embodiments enable determination of a view yield or promo yield 504.Yield 504 can be considered in determining whether a spot has a highertune-in rate, and therefore is presumed more effective. Accordingly, itis possible to enable analysis of yield and return on investment of thepromo spots, valuable in establishing proof of performance. For example,embodiments can determine that promo yield can be significantly improvedby placement of promo spots on the same day of the week as the promotedprogram. Additionally, embodiments can determine that same-day promoyield and 7-day advance promo yield can significantly exceed overallaverage yield. Analysis of viewing data therefore enables calculation ofan effective yield of program viewers from specific promo airings.Furthermore, it is possible to optimize promo scheduling, based on dayof the week and/or time of the day, valuable for business intelligence.

Embodiments can identify, capture, and account for variables that affectthe performance of promos. Analysis thereby provides a valuable view ofindividual promo performance. For example, promos can be aired atvarious times of the day and days of the week. By indexing and/orweighting each airing of an individual promo, it is possible todetermine how each promo benefits from a given day or time placementmix. Such information is valuable for adjusting prices charged for promoor ad placement, customized for the particular promo or ad and day ortime placement. Proof of performance and business intelligence canthereby be optimized.

Accordingly, embodiments enable determination of return on investmentincluding expected yield for a given content of the promo or ad, and fora given insertion day/time mix. Therefore, promotional dollars andcross-channel value can be maximized.

Embodiments can use programmable machine learning algorithms forreal-time prediction of future viewer behavior patterns. Examplealgorithms can be implemented as a Bayesian network or a Hidden MarkovModel, for example. Algorithms can acquire channel tuning and viewingactivities, and automatically compute an optimal viewing pattern graphout of the universe of all viewing activities, as a function of time.The algorithms can output a predicted or maximum likelihood estimator ofchannel/service/time state vector. Cluster analysis can be performed togenerate a list of predicted channel/services by groups, clusteringusers into groups that are likely to watch or tune to a specifieddestination at a specified time. Embodiments can be adapted forapplications where future behavior, e.g., in a probabilistic sense, canbe leveraged to improve service, value, or revenues. Results of machineanalysis can be utilized for optimal ad placement, maximizing customerimpression yields, increasing value to advertisers by providing agreater known probability of reaching a larger audience, and offeringsuch business intelligence at a premium charge.

The algorithms can be implemented on a system, and can be implemented asa distributed stochastic learning agent model that operates on embeddeddevices such as STBs and other devices. Client agent processes can bedistributed and executed concurrently on multiple embedded devicesparticipating as a system. An initialization phase can be used todistribute (scatter) operating parameters to embedded devices for themachine learning algorithm. Output from the distributed agents can besent to a remote system, e.g., a server system in the Cloud, forprocessing that can include cluster analysis.

FIG. 6 is a schematic diagram of an example computer system 600 used toimplement embodiments of the STB 102, headend 101, data collectionsystem 301, data collectors 108, data warehouse 110, and/or othersystems. Various aspects of the various embodiments can be implementedby software, firmware, hardware, or a combination thereof. FIG. 6illustrates an example computer system 600 in which an embodiment, orportions thereof, can be implemented as computer-readable code. Variousembodiments are described in terms of this example computer system 600.After reading this description, it will become apparent to a personskilled in the relevant art how to implement embodiments using othercomputer systems and/or computer architectures.

Computer system 600 includes one or more processors, such as processor604. Processor 604 can be a special purpose or a general purposeprocessor. Processor 604 is connected to a communication infrastructure606 (for example, a bus or network).

Computer system 600 also includes a main memory 608, preferably randomaccess memory (RAM), and may also include a secondary memory 610.Secondary memory 610 may include, for example, a hard disk drive 612and/or a removable storage drive 614. Removable storage drive 614 maycomprise a floppy disk drive, a magnetic tape drive, an optical diskdrive, a flash memory, or the like. The removable storage drive 614reads from and/or writes to a removable storage unit 618 in a well knownmanner. Removable storage unit 618 may comprise a floppy disk, magnetictape, optical disk, etc. which is read by and written to by removablestorage drive 614. As will be appreciated by persons skilled in therelevant art(s), removable storage unit 618 includes a tangible computerreadable storage medium having stored therein computer software and/ordata.

In alternative implementations, secondary memory 610 may include othersimilar means for allowing computer programs or other instructions to beloaded into computer system 600. Such means may include, for example, aremovable storage unit 622 and an interface 620. Examples of such meansmay include a program cartridge and cartridge interface (such as thatfound in video game devices), a removable memory chip (such as an EPROM,or PROM) and associated socket, and other removable storage units 622and interfaces 620 which allow software and data to be transferred fromthe removable storage unit 622 to computer system 600.

Computer system 600 may also include a communications interface 624.Communications interface 624 allows software and data to be transferredbetween computer system 600 and external devices. Communicationsinterface 624 may include a modem, a network interface (e.g., anEthernet card), a communications port, a PCMCIA slot and card, or thelike. Software and data transferred via communications interface 624 areprovided to communications interface 624 via a communications path 626.Communications path 626 may be implemented using wire or cable, fiberoptics, a phone line, a cellular phone link, an RF link or othercommunications channels.

In this document, the terms “computer program medium” and “computerusable medium” are used to generally refer to media such as removablestorage unit 618, removable storage unit 622, and a hard disk installedin hard disk drive 612. Computer program medium and computer usablemedium can also refer to memories, such as main memory 608 and secondarymemory 610, which can be memory semiconductors (e.g. DRAMs, etc.). Thesecomputer program products are means for providing software to computersystem 600.

Computer programs (also called computer control logic) are stored inmain memory 608 and/or secondary memory 610. Computer programs may alsobe received via communications interface 624. Such computer programs,when executed, enable computer system 600 to implement embodiments asdiscussed herein, such as the system described above. In particular, thecomputer programs, when executed, enable processor 604 to implement theprocesses of embodiments. Accordingly, such computer programs representcontrollers of the computer system 600. Where embodiments areimplemented using software, the software may be stored in a computerprogram product and loaded into computer system 600 using removablestorage drive 614, interface 620, hard drive 612 or communicationsinterface 624.

Described above are systems, apparatuses, and methods for use of viewingdata, and applications thereof. It is to be appreciated that theDetailed Description section, and not the Abstract, is intended to beused to interpret the claims. The Abstract may set forth one or more butnot all exemplary embodiments of the present invention as contemplatedby the inventors, and thus, are not intended to limit the presentinvention and the appended claims in any way.

Embodiments have been described above with the aid of functionalbuilding blocks illustrating the implementation of specified functionsand relationships thereof. The boundaries of these functional buildingblocks have been arbitrarily defined herein for the convenience of thedescription. Alternate boundaries can be defined so long as thespecified functions and relationships thereof are appropriatelyperformed.

The foregoing description of the specific embodiments will so fullyreveal the general nature of the invention that others can, by applyingknowledge within the skill of the art, readily modify and/or adapt forvarious applications such specific embodiments, without undueexperimentation, without departing from the general concept of thepresent invention. Therefore, such adaptations and modifications areintended to be within the meaning and range of equivalents of thedisclosed embodiments, based on the teaching and guidance presentedherein. It is to be understood that the phraseology or terminologyherein is for the purpose of description and not of limitation, suchthat the terminology or phraseology of the present specification is tobe interpreted by the skilled artisan in light of the teachings andguidance.

The breadth and scope of the present invention should not be limited byany of the above-described exemplary embodiments, but should be definedonly in accordance with the following claims and their equivalents.

1. A method, comprising: periodically monitoring data representingactions taken relative to media; combining the data with attributes; andanalyzing the combined data to determine patterns.
 2. The method ofclaim 1, wherein the data comprises interaction with the Internet. 3.The method of claim 1, further comprising receiving the data associatedwith a mobile device.
 4. The method of claim 1, further comprisingreceiving the data associated with a television.
 5. The method of claim1, further comprising centrally collecting the data by a datarepository.
 6. The method of claim 1, further comprising storing thedata on a set-top-box in memory.
 7. The method of claim 6, furthercomprising transmitting the data from the set-top box periodically to adata repository.
 8. The method of claim 6, further comprisingtransmitting the data from the set-top box based on storing a thresholdamount of data in the memory.
 9. The method of claim 6, furthercomprising transmitting the data from the set-top box based on atransmission triggering event.
 10. The method of claim 6, furthercomprising transmitting the data from the set-top box at randomintervals.
 11. The method of claim 6, further comprising collecting thedata on a distributed basis from a plurality of set top boxes.
 12. Themethod of claim 11, further comprising selecting the plurality of settop boxes based on the attributes.
 13. The method of claim 11, furthercomprising randomly selecting the plurality of set top boxes from apercentage of total set top boxes.
 14. The method of claim 11, furthercomprising selecting the plurality of set top boxes based on a Booleancombination of factors.
 15. The method of claim 1, further comprisingbroadcasting a configuration file to a plurality of set top boxes,wherein the configuration file informs each set top box how to monitorand upload data.
 16. The method of claim 1, wherein the data comprises adwelling time associated with a duration of an activity.
 17. The methodof claim 1, wherein the data comprises commands received by a set topbox.
 18. The method of claim 17, wherein said commands relate to viewingthe media.
 19. The method of claim 18, wherein viewing the mediaincludes changing a channel.
 20. The method of claim 18, wherein viewingthe media includes accessing a website.
 21. The method of claim 17,further comprising collecting data about equipment connected to theset-top box.
 22. The method of claim 1, further comprising collectingdata about equipment used for the actions taken relative to the media.23. The method of claim 1, further comprising anonymizing the combineddata.
 24. The method of claim 1, further comprising determining a sampleconfiguration of data sources to monitor; and collecting the data fromthe sample configuration.
 25. The method of claim 24, further comprisingdetermining if the sample configuration includes desired combined data;and selecting a new sample configuration based on the determining. 26.The method of claim 25, further comprising determining and selecting thenew sample configuration in near real-time.
 27. The method of claim 1,further comprising determining audience composition and behavior basedon the combined data.
 28. The method of claim 27, wherein behaviorincludes tuneaways associated with the media over time.
 29. The methodof claim 27, wherein behavior includes Internet activity associated withthe media over time.
 30. The method of claim 27, wherein behaviorincludes loyalty to a given type of media.
 31. The method of claim 1,further comprising timestamping the data; and correlating the timestampswith media content logs.
 32. The method of claim 1, wherein analyzingthe combined data includes determining a time slot that will optimize anadvertiser's exposure to a specific demographic.
 33. The method of claim1, wherein analyzing the combined data includes determining the presenceof target data specified by an advertiser in satisfaction of a proof ofperformance.
 34. The method of claim 1, wherein analyzing the combineddata includes determining a yield of combined data associated withtarget data specified by an advertiser.
 35. The method of claim 1,wherein analyzing the combined data includes determining a first set ofactions relative to promotional media over time; and determining asecond set of actions taken relative to promoted media corresponding tothe promotional media.
 36. The method of claim 35, further comprisingdetermining a yield of the promotional media.
 37. The method of claim36, further comprising determining the yield associated with aninsertion day and time placement mix of media.
 38. The method of claim1, further comprising determining an expected yield of media based onmedia content and an insertion day and time placement mix.
 39. Themethod of claim 1, wherein the attributes are demographic attributes.40. A method, comprising: receiving a configuration file, wherein theconfiguration file informs how to monitor and upload data; periodicallymonitoring data representing actions taken relative to media, includinginteraction with the Internet and media displayed on a television,wherein the data comprises a dwelling time associated with a duration ofan activity; storing the data in memory, wherein the data has anassociated timestamp; transmitting the data periodically to a datarepository; and analyzing the data to determine patterns.
 41. A method,comprising: selecting a plurality of set top boxes from within aservice-provider managed network based on targeted attributes;monitoring data according to a configuration file broadcast to theplurality of set top boxes, the data representing actions taken relativeto media at the plurality of set top boxes; and analyzing the data andtargeted attributes to determine a predicted yield for potentialtargeted attributes and media.
 42. An apparatus, comprising: a set topbox for monitoring and transmitting data according to a configurationfile received from a service-provider managed network, the datarepresenting actions taken relative to media; wherein the configurationfile instructs the set top box to monitor the data based on targetedattributes associated with the set top box, and transmit the data to adata repository based on monitoring a triggering event.